RESUMO
One of the most popular techniques for computer-assisted solution estimates for magnetics and gravity field data is Werner deconvolution. The approaches frequently produce erratic results and may not always forecast the maximum number of the geologic entity that produces them due to the intrinsic instability of potential field data. This led to the application of the K-means machine learning algorithm to further enhance the detection of the geologic potential field-generated bodies. Two substances that resembled dikes were combined to form a synthetic magnetic model. Random noise was added to the synthetic data, to make the solutions a bit more complex. Werner deconvolution technique was applied to the synthetic model to generate solutions. K-means unsupervised machine learning algorithm was applied to the generated solutions created by the synthetic data. We further applied this algorithm to real data sets from a mining site. The clustering result shows a good spatial correspondence with the geologic model, and the method was able to estimate the precise location and depth of the dike bodies. The proposed method is entirely data-driven and has proven to work in the presence of noise.
RESUMO
OBJECTIVES: Faced with a costly and demanding learning curve of surgical skills acquisition, the growing necessity for improved surgical curricula has now become irrefutable. We took this opportunity to formulate a teaching framework with the capacity to provide holistic surgical education at the undergraduate level. SETTING: Data collection was conducted in all the relevant healthcare centres the participants worked in. Where this was not possible, interviews were held in quiet public places. PARTICIPANTS: We performed an in-depth retrospective evaluation of a proposed curriculum, through semi-structured interviews with 10 participants. A targeted sampling technique was employed in order to identify senior academics with specialist knowledge in surgical education. Recruitment was ceased on reaching data saturation after which thematic data analysis was performed using NVivo 11. RESULTS: Thematic analysis yielded a total of 4 main themes and 29 daughter nodes. Majority of study participants agreed that the current landscape of basic surgical education is deficient at multiple levels. While simulation cannot replace surgical skills acquisition taking place in operating rooms, it can be catalytic in the transition of students to postgraduate training. Our study concluded that a standardised format of surgical teaching is essential, and that the Integrated Generation 4 (IG4) framework provides an excellent starting point. CONCLUSIONS: Through expert opinion, IG4 has been validated for its capacity to effectively accommodate learning in a safer and more efficacious environment. Moreover, we support that through dissemination of IG4, we can instil a sense of motivation to students as well as develop robust data sets, which will be amenable to data analysis through the application of more sophisticated methodologies.
